Postnatal environmental exposures, particularly those found in household products and dietary intake, along with specific serum metabolomics profiles, are significantly associated with the BMI Z-score of children aged 6-11 years. Higher concentrations of certain metabolites in serum, reflecting exposure to chemical classes or metals, will correlate with variations in BMI Z-score, controlling for age and other relevant covariates. High-dimensional metabolomics data can reveal comprehensive biochemical profiles that reflect environmental exposures and metabolic states. Not only that, but some metabolites associated with chemical exposures and dietary patterns can serve as biomarkers for the risk of developing obesity.
Research indicates that postnatal exposure to endocrine-disrupting chemicals (EDCs) such as phthalates, bisphenol A (BPA), and polychlorinated biphenyls (PCBs) can significantly influence body weight and metabolic health (Junge et al., 2018). These chemicals, commonly found in household products and absorbed through dietary intake, are linked to detrimental effects on body weight and metabolic health in children. This hormonal interference can lead to an increased body mass index (BMI) in children, suggesting a potential pathway through which exposure to these chemicals contributes to the development of obesity.
A longitudinal study on Japanese children examined the impact of postnatal exposure (first two years of life) to p,p’-dichlorodiphenyltrichloroethane (p,p’-DDT) and p,p’-dichlorodiphenyldichloroethylene (p,p’-DDE) through breastfeeding (Plouffe et al., 2020). The findings revealed that higher levels of these chemicals in breast milk were associated with increased BMI at 42 months of age. DDT and DDE may interfere with hormonal pathways related to growth and development. These chemicals can mimic or disrupt hormones that regulate metabolism and fat accumulation. This study highlights the importance of understanding how persistent organic pollutants can affect early childhood growth and development.
The study by Harley et al. (2013) investigates the association between prenatal and postnatal Bisphenol A (BPA) exposure and various body composition metrics in children aged 9 years from the CHAMACOS cohort. The study found that higher prenatal BPA exposure was linked to a decrease in BMI and body fat percentages in girls but not boys, suggesting sex-specific effects. Conversely, BPA levels measured at age 9 were positively associated with increased adiposity in both genders, highlighting the different impacts of exposure timing on childhood development.
The 2022 study 2022 study by Uldbjerg et al. explored the effects of combined exposures to multiple EDCs, suggesting that mixtures of these chemicals can have additive or synergistic effects on BMI and obesity risk. Humans are typically exposed to a mixture of chemicals rather than individual EDCs, making it crucial to understand how these mixtures might interact. The research highlighted that the interaction between different EDCs can lead to additive (where the effects simply add up) or even synergistic (where the combined effect is greater than the sum of their separate effects) outcomes. These interactions can significantly amplify the risk factors associated with obesity and metabolic disorders in children. The dose-response relationship found that even low-level exposure to multiple EDCs could result in significant health impacts due to their combined effects.
These studies collectively illustrate the critical role of environmental EDCs in shaping metabolic health outcomes in children, highlighting the necessity for ongoing research and policy intervention to mitigate these risks.
This study will utilize data from the subcohort of 1301 mother-child pairs in the HELIX study, who are which aged 6-11 years for whom complete exposure and outcome data were available. Exposure data included detailed dietary records after pregnancy and concentrations of various chemicals like BPA and PCBs in child blood samples. There are categorical and numerical variables, which will include both demographic details and biochemical measurements. This dataset allows for robust statistical analysis to identify potential associations between EDC exposure and changes in BMI Z-scores, considering confounding factors such as age, gender, and socioeconomic status. There are no missing data so there is not need to impute the information. Child BMI Z-scores were calculated based on WHO growth standards.
load(paste0(work.dir, "/HELIX.RData"))
filtered_codebook <- codebook %>%
filter(domain %in% c("Chemicals", "Lifestyles") & period == "Postnatal" & subfamily != "Allergens")
kable(filtered_codebook, align = "c", format = "html") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"), full_width = F)
| variable_name | domain | family | subfamily | period | location | period_postnatal | description | var_type | transformation | labels | labelsshort | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| h_bfdur_Ter | h_bfdur_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Breastfeeding duration (weeks) | factor | Tertiles | Breastfeeding | Breastfeeding |
| hs_bakery_prod_Ter | hs_bakery_prod_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: bakery products (hs_cookies + hs_pastries) | factor | Tertiles | Bakery prod | BakeProd |
| hs_beverages_Ter | hs_beverages_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: beverages (hs_dietsoda+hs_soda) | factor | Tertiles | Soda | Soda |
| hs_break_cer_Ter | hs_break_cer_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: breakfast cereal (hs_sugarcer+hs_othcer) | factor | Tertiles | BF cereals | BFcereals |
| hs_caff_drink_Ter | hs_caff_drink_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Drinks a caffeinated or æenergy drink (eg coca-cola, diet-coke, redbull) | factor | Tertiles | Caffeine | Caffeine |
| hs_dairy_Ter | hs_dairy_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: dairy (hs_cheese + hs_milk + hs_yogurt+ hs_probiotic+ hs_desert) | factor | Tertiles | Dairy | Dairy |
| hs_fastfood_Ter | hs_fastfood_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Visits a fast food restaurant/take away | factor | Tertiles | Fastfood | Fastfood |
| hs_KIDMED_None | hs_KIDMED_None | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Sum of KIDMED indices, without index9 | numeric | None | KIDMED | KIDMED |
| hs_mvpa_prd_alt_None | hs_mvpa_prd_alt_None | Lifestyles | Lifestyle | Physical activity | Postnatal | NA | NA | Clean & Over-reporting of Moderate-to-Vigorous Physical Activity (min/day) | numeric | None | PA | PA |
| hs_org_food_Ter | hs_org_food_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Eats organic food | factor | Tertiles | Organicfood | Organicfood |
| hs_proc_meat_Ter | hs_proc_meat_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: processed meat (hs_coldmeat+hs_ham) | factor | Tertiles | Processed meat | ProcMeat |
| hs_readymade_Ter | hs_readymade_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Eats a æready-made supermarket meal | factor | Tertiles | Ready made food | ReadyFood |
| hs_sd_wk_None | hs_sd_wk_None | Lifestyles | Lifestyle | Physical activity | Postnatal | NA | NA | sedentary behaviour (min/day) | numeric | None | Sedentary | Sedentary |
| hs_total_bread_Ter | hs_total_bread_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: bread (hs_darkbread+hs_whbread) | factor | Tertiles | Bread | Bread |
| hs_total_cereal_Ter | hs_total_cereal_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: cereal (hs_darkbread + hs_whbread + hs_rice_pasta + hs_sugarcer + hs_othcer + hs_rusks) | factor | Tertiles | Cereals | Cereals |
| hs_total_fish_Ter | hs_total_fish_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: fish and seafood (hs_canfish+hs_oilyfish+hs_whfish+hs_seafood) | factor | Tertiles | Fish | Fish |
| hs_total_fruits_Ter | hs_total_fruits_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: fruits (hs_canfruit+hs_dryfruit+hs_freshjuice+hs_fruits) | factor | Tertiles | Fruits | Fruits |
| hs_total_lipids_Ter | hs_total_lipids_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: Added fat | factor | Tertiles | Diet fat | Diet fat |
| hs_total_meat_Ter | hs_total_meat_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: meat (hs_coldmeat+hs_ham+hs_poultry+hs_redmeat) | factor | Tertiles | Meat | Meat |
| hs_total_potatoes_Ter | hs_total_potatoes_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: potatoes (hs_frenchfries+hs_potatoes) | factor | Tertiles | Potatoes | Potatoes |
| hs_total_sweets_Ter | hs_total_sweets_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: sweets (hs_choco + hs_sweets + hs_sugar) | factor | Tertiles | Sweets | Sweets |
| hs_total_veg_Ter | hs_total_veg_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: vegetables (hs_cookveg+hs_rawveg) | factor | Tertiles | Vegetables | Vegetables |
| hs_total_yog_Ter | hs_total_yog_Ter | Lifestyles | Lifestyle | Diet | Postnatal | NA | NA | Food group: yogurt (hs_yogurt+hs_probiotic) | factor | Tertiles | Yogurt | Yogurt |
| hs_dif_hours_total_None | hs_dif_hours_total_None | Lifestyles | Lifestyle | Sleep | Postnatal | NA | NA | Total hours of sleep (mean weekdays and night) | numeric | None | Sleep | Sleep |
| hs_as_c_Log2 | hs_as_c_Log2 | Chemicals | Metals | As | Postnatal | NA | NA | Arsenic (As) in child | numeric | Logarithm base 2 | As | As |
| hs_cd_c_Log2 | hs_cd_c_Log2 | Chemicals | Metals | Cd | Postnatal | NA | NA | Cadmium (Cd) in child | numeric | Logarithm base 2 | Cd | Cd |
| hs_co_c_Log2 | hs_co_c_Log2 | Chemicals | Metals | Co | Postnatal | NA | NA | Cobalt (Co) in child | numeric | Logarithm base 2 | Co | Co |
| hs_cs_c_Log2 | hs_cs_c_Log2 | Chemicals | Metals | Cs | Postnatal | NA | NA | Caesium (Cs) in child | numeric | Logarithm base 2 | Cs | Cs |
| hs_cu_c_Log2 | hs_cu_c_Log2 | Chemicals | Metals | Cu | Postnatal | NA | NA | Copper (Cu) in child | numeric | Logarithm base 2 | Cu | Cu |
| hs_hg_c_Log2 | hs_hg_c_Log2 | Chemicals | Metals | Hg | Postnatal | NA | NA | Mercury (Hg) in child | numeric | Logarithm base 2 | Hg | Hg |
| hs_mn_c_Log2 | hs_mn_c_Log2 | Chemicals | Metals | Mn | Postnatal | NA | NA | Manganese (Mn) in child | numeric | Logarithm base 2 | Mn | Mn |
| hs_mo_c_Log2 | hs_mo_c_Log2 | Chemicals | Metals | Mo | Postnatal | NA | NA | Molybdenum (Mo) in child | numeric | Logarithm base 2 | Mo | Mo |
| hs_pb_c_Log2 | hs_pb_c_Log2 | Chemicals | Metals | Pb | Postnatal | NA | NA | Lead (Pb) in child | numeric | Logarithm base 2 | Pb | Pb |
| hs_tl_cdich_None | hs_tl_cdich_None | Chemicals | Metals | Tl | Postnatal | NA | NA | Dichotomous variable of thallium (Tl) in child | factor | None | Tl | Tl |
| hs_dde_cadj_Log2 | hs_dde_cadj_Log2 | Chemicals | Organochlorines | DDE | Postnatal | NA | NA | Dichlorodiphenyldichloroethylene (DDE) in child adjusted for lipids | numeric | Logarithm base 2 | DDE | DDE |
| hs_ddt_cadj_Log2 | hs_ddt_cadj_Log2 | Chemicals | Organochlorines | DDT | Postnatal | NA | NA | Dichlorodiphenyltrichloroethane (DDT) in child adjusted for lipids | numeric | Logarithm base 2 | DDT | DDT |
| hs_hcb_cadj_Log2 | hs_hcb_cadj_Log2 | Chemicals | Organochlorines | HCB | Postnatal | NA | NA | Hexachlorobenzene (HCB) in child adjusted for lipids | numeric | Logarithm base 2 | HCB | HCB |
| hs_pcb118_cadj_Log2 | hs_pcb118_cadj_Log2 | Chemicals | Organochlorines | PCBs | Postnatal | NA | NA | Polychlorinated biphenyl -118 (PCB-118) in child adjusted for lipids | numeric | Logarithm base 2 | PCB 118 | PCB118 |
| hs_pcb138_cadj_Log2 | hs_pcb138_cadj_Log2 | Chemicals | Organochlorines | PCBs | Postnatal | NA | NA | Polychlorinated biphenyl-138 (PCB-138) in child adjusted for lipids | numeric | Logarithm base 2 | PCB 138 | PCB138 |
| hs_pcb153_cadj_Log2 | hs_pcb153_cadj_Log2 | Chemicals | Organochlorines | PCBs | Postnatal | NA | NA | Polychlorinated biphenyl-153 (PCB-153) in child adjusted for lipids | numeric | Logarithm base 2 | PCB 153 | PCB153 |
| hs_pcb170_cadj_Log2 | hs_pcb170_cadj_Log2 | Chemicals | Organochlorines | PCBs | Postnatal | NA | NA | Polychlorinated biphenyl-170 (PCB-170) in child adjusted for lipids | numeric | Logarithm base 2 | PCB 170 | PCB170 |
| hs_pcb180_cadj_Log2 | hs_pcb180_cadj_Log2 | Chemicals | Organochlorines | PCBs | Postnatal | NA | NA | Polychlorinated biphenyl-180 (PCB-180) in child adjusted for lipids | numeric | Logarithm base 2 | PCB 180 | PCB180 |
| hs_sumPCBs5_cadj_Log2 | hs_sumPCBs5_cadj_Log2 | Chemicals | Organochlorines | PCBs | Postnatal | NA | NA | Sum of PCBs in child adjusted for lipids (4 cohorts) | numeric | Logarithm base 2 | PCBs | SumPCB |
| hs_dep_cadj_Log2 | hs_dep_cadj_Log2 | Chemicals | Organophosphate pesticides | DEP | Postnatal | NA | NA | Diethyl phosphate (DEP) in child adjusted for creatinine | numeric | Logarithm base 2 | DEP | DEP |
| hs_detp_cadj_Log2 | hs_detp_cadj_Log2 | Chemicals | Organophosphate pesticides | DETP | Postnatal | NA | NA | Diethyl thiophosphate (DETP) in child adjusted for creatinine | numeric | Logarithm base 2 | DETP | DETP |
| hs_dmdtp_cdich_None | hs_dmdtp_cdich_None | Chemicals | Organophosphate pesticides | DMDTP | Postnatal | NA | NA | Dichotomous variable of dimethyl dithiophosphate (DMDTP) in child | factor | None | DMDTP | DMDTP |
| hs_dmp_cadj_Log2 | hs_dmp_cadj_Log2 | Chemicals | Organophosphate pesticides | DMP | Postnatal | NA | NA | Dimethyl phosphate (DMP) in child adjusted for creatinine | numeric | Logarithm base 2 | DMP | DMP |
| hs_dmtp_cadj_Log2 | hs_dmtp_cadj_Log2 | Chemicals | Organophosphate pesticides | DMTP | Postnatal | NA | NA | Dimethyl thiophosphate (DMTP) in child adjusted for creatinine | numeric | Logarithm base 2 | DMDTP | DMTP |
| hs_pbde153_cadj_Log2 | hs_pbde153_cadj_Log2 | Chemicals | Polybrominated diphenyl ethers (PBDE) | PBDE153 | Postnatal | NA | NA | Polybrominated diphenyl ether-153 (PBDE-153) in child adjusted for lipids | numeric | Logarithm base 2 | PBDE 153 | PBDE153 |
| hs_pbde47_cadj_Log2 | hs_pbde47_cadj_Log2 | Chemicals | Polybrominated diphenyl ethers (PBDE) | PBDE47 | Postnatal | NA | NA | Polybrominated diphenyl ether-47 (PBDE-47) in child adjusted for lipids | numeric | Logarithm base 2 | PBDE 47 | PBDE47 |
| hs_pfhxs_c_Log2 | hs_pfhxs_c_Log2 | Chemicals | Per- and polyfluoroalkyl substances (PFAS) | PFHXS | Postnatal | NA | NA | Perfluorohexane sulfonate (PFHXS) in child | numeric | Logarithm base 2 | PFHXS | PFHXS |
| hs_pfna_c_Log2 | hs_pfna_c_Log2 | Chemicals | Per- and polyfluoroalkyl substances (PFAS) | PFNA | Postnatal | NA | NA | Perfluorononanoate (PFNA) in child | numeric | Logarithm base 2 | PFNA | PFNA |
| hs_pfoa_c_Log2 | hs_pfoa_c_Log2 | Chemicals | Per- and polyfluoroalkyl substances (PFAS) | PFOA | Postnatal | NA | NA | Perfluorooctanoate (PFOA) in child | numeric | Logarithm base 2 | PFOA | PFOA |
| hs_pfos_c_Log2 | hs_pfos_c_Log2 | Chemicals | Per- and polyfluoroalkyl substances (PFAS) | PFOS | Postnatal | NA | NA | Perfluorooctane sulfonate (PFOS) in child | numeric | Logarithm base 2 | PFOS | PFOS |
| hs_pfunda_c_Log2 | hs_pfunda_c_Log2 | Chemicals | Per- and polyfluoroalkyl substances (PFAS) | PFUNDA | Postnatal | NA | NA | Perfluoroundecanoate (PFUNDA) in child | numeric | Logarithm base 2 | PFUNDA | PFUNDA |
| hs_bpa_cadj_Log2 | hs_bpa_cadj_Log2 | Chemicals | Phenols | BPA | Postnatal | NA | NA | Bisphenol A (BPA) in child adjusted for creatinine | numeric | Logarithm base 2 | BPA | BPA |
| hs_bupa_cadj_Log2 | hs_bupa_cadj_Log2 | Chemicals | Phenols | BUPA | Postnatal | NA | NA | N-Butyl paraben (BUPA) in child adjusted for creatinine | numeric | Logarithm base 2 | BUPA | BUPA |
| hs_etpa_cadj_Log2 | hs_etpa_cadj_Log2 | Chemicals | Phenols | ETPA | Postnatal | NA | NA | Ethyl paraben (ETPA) in child adjusted for creatinine | numeric | Logarithm base 2 | ETPA | ETPA |
| hs_mepa_cadj_Log2 | hs_mepa_cadj_Log2 | Chemicals | Phenols | MEPA | Postnatal | NA | NA | Methyl paraben (MEPA) in child adjusted for creatinine | numeric | Logarithm base 2 | MEPA | MEPA |
| hs_oxbe_cadj_Log2 | hs_oxbe_cadj_Log2 | Chemicals | Phenols | OXBE | Postnatal | NA | NA | Oxybenzone (OXBE) in child adjusted for creatinine | numeric | Logarithm base 2 | OXBE | OXBE |
| hs_prpa_cadj_Log2 | hs_prpa_cadj_Log2 | Chemicals | Phenols | PRPA | Postnatal | NA | NA | Propyl paraben (PRPA) in child adjusted for creatinine | numeric | Logarithm base 2 | PRPA | PRPA |
| hs_trcs_cadj_Log2 | hs_trcs_cadj_Log2 | Chemicals | Phenols | TRCS | Postnatal | NA | NA | Triclosan (TRCS) in child adjusted for creatinine | numeric | Logarithm base 2 | TRCS | TRCS |
| hs_mbzp_cadj_Log2 | hs_mbzp_cadj_Log2 | Chemicals | Phthalates | MBZP | Postnatal | NA | NA | Mono benzyl phthalate (MBzP) in child adjusted for creatinine | numeric | Logarithm base 2 | MBZP | MBZP |
| hs_mecpp_cadj_Log2 | hs_mecpp_cadj_Log2 | Chemicals | Phthalates | MECPP | Postnatal | NA | NA | Mono-2-ethyl 5-carboxypentyl phthalate (MECPP) in child adjusted for creatinine | numeric | Logarithm base 2 | MECPP | MECPP |
| hs_mehhp_cadj_Log2 | hs_mehhp_cadj_Log2 | Chemicals | Phthalates | MEHHP | Postnatal | NA | NA | Mono-2-ethyl-5-hydroxyhexyl phthalate (MEHHP) in child adjusted for creatinine | numeric | Logarithm base 2 | MEHHP | MEHHP |
| hs_mehp_cadj_Log2 | hs_mehp_cadj_Log2 | Chemicals | Phthalates | MEHP | Postnatal | NA | NA | Mono-2-ethylhexyl phthalate (MEHP) in child adjusted for creatinine | numeric | Logarithm base 2 | MEHP | MEHP |
| hs_meohp_cadj_Log2 | hs_meohp_cadj_Log2 | Chemicals | Phthalates | MEOHP | Postnatal | NA | NA | Mono-2-ethyl-5-oxohexyl phthalate (MEOHP) in child adjusted for creatinine | numeric | Logarithm base 2 | MEOHP | MEOHP |
| hs_mep_cadj_Log2 | hs_mep_cadj_Log2 | Chemicals | Phthalates | MEP | Postnatal | NA | NA | Monoethyl phthalate (MEP) in child adjusted for creatinine | numeric | Logarithm base 2 | MEP | MEP |
| hs_mibp_cadj_Log2 | hs_mibp_cadj_Log2 | Chemicals | Phthalates | MIBP | Postnatal | NA | NA | Mono-iso-butyl phthalate (MiBP) in child adjusted for creatinine | numeric | Logarithm base 2 | MIBP | MIBP |
| hs_mnbp_cadj_Log2 | hs_mnbp_cadj_Log2 | Chemicals | Phthalates | MNBP | Postnatal | NA | NA | Mono-n-butyl phthalate (MnBP) in child adjusted for creatinine | numeric | Logarithm base 2 | MNBP | MNBP |
| hs_ohminp_cadj_Log2 | hs_ohminp_cadj_Log2 | Chemicals | Phthalates | OHMiNP | Postnatal | NA | NA | Mono-4-methyl-7-hydroxyoctyl phthalate (OHMiNP) in child adjusted for creatinine | numeric | Logarithm base 2 | OHMiNP | OHMiNP |
| hs_oxominp_cadj_Log2 | hs_oxominp_cadj_Log2 | Chemicals | Phthalates | OXOMINP | Postnatal | NA | NA | Mono-4-methyl-7-oxooctyl phthalate (OXOMiNP) in child adjusted for creatinine | numeric | Logarithm base 2 | OXOMINP | OXOMINP |
| hs_sumDEHP_cadj_Log2 | hs_sumDEHP_cadj_Log2 | Chemicals | Phthalates | DEHP | Postnatal | NA | NA | Sum of DEHP metabolites (µg/g) in child adjusted for creatinine | numeric | Logarithm base 2 | DEHP | SumDEHP |
| FAS_cat_None | FAS_cat_None | Chemicals | Social and economic capital | Economic capital | Postnatal | NA | NA | Family affluence score | factor | None | Family affluence | FamAfl |
| hs_contactfam_3cat_num_None | hs_contactfam_3cat_num_None | Chemicals | Social and economic capital | Social capital | Postnatal | NA | NA | scoial capital: family friends | factor | None | Social contact | SocCont |
| hs_hm_pers_None | hs_hm_pers_None | Chemicals | Social and economic capital | Social capital | Postnatal | NA | NA | How many people live in your home? | numeric | None | House crowding | HouseCrow |
| hs_participation_3cat_None | hs_participation_3cat_None | Chemicals | Social and economic capital | Social capital | Postnatal | NA | NA | social capital: structural | factor | None | Social participation | SocPartic |
| hs_cotinine_cdich_None | hs_cotinine_cdich_None | Chemicals | Tobacco Smoke | Cotinine | Postnatal | NA | NA | Dichotomous variable of cotinine in child | factor | None | Cotinine | Cotinine |
| hs_globalexp2_None | hs_globalexp2_None | Chemicals | Tobacco Smoke | Tobacco Smoke | Postnatal | NA | NA | Global exposure of the child to ETS (2 categories) | factor | None | ETS | ETS |
| hs_smk_parents_None | hs_smk_parents_None | Chemicals | Tobacco Smoke | Tobacco Smoke | Postnatal | NA | NA | Tobacco Smoke status of parents (both) | factor | None | Smoking_parents | SmokPar |
Lifestyle_Exposures <- filtered_codebook$variable_name[filtered_codebook$domain=="Lifestyles"]
lifestyle_exposome <- exposome %>%
select(all_of(Lifestyle_Exposures))
summarytools::view(dfSummary(lifestyle_exposome, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | h_bfdur_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 2 | hs_bakery_prod_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 3 | hs_beverages_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 4 | hs_break_cer_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 5 | hs_caff_drink_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 6 | hs_dairy_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 7 | hs_fastfood_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 8 | hs_KIDMED_None [numeric] |
|
13 distinct values | 0 (0.0%) | ||||||||||||||||
| 9 | hs_mvpa_prd_alt_None [numeric] |
|
847 distinct values | 0 (0.0%) | ||||||||||||||||
| 10 | hs_org_food_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 11 | hs_proc_meat_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 12 | hs_readymade_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 13 | hs_sd_wk_None [numeric] |
|
368 distinct values | 0 (0.0%) | ||||||||||||||||
| 14 | hs_total_bread_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 15 | hs_total_cereal_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 16 | hs_total_fish_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 17 | hs_total_fruits_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 18 | hs_total_lipids_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 19 | hs_total_meat_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 20 | hs_total_potatoes_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 21 | hs_total_sweets_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 22 | hs_total_veg_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 23 | hs_total_yog_Ter [factor] |
|
|
0 (0.0%) | ||||||||||||||||
| 24 | hs_dif_hours_total_None [numeric] |
|
437 distinct values | 0 (0.0%) |
Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10
#separate numeric and categorical data
numeric_lifestyle <- lifestyle_exposome %>%
select(where(is.numeric))
numeric_lifestyle_long <- pivot_longer(
numeric_lifestyle,
cols = everything(),
names_to = "variable",
values_to = "value"
)
unique_numerical_vars <- unique(numeric_lifestyle_long$variable)
num_plots <- lapply(unique_numerical_vars, function(var) {
data <- filter(numeric_lifestyle_long, variable == var)
p <- ggplot(data, aes(x = value)) +
geom_histogram(bins = 30, fill = "blue") +
labs(title = paste("Histogram of", var), x = "Value", y = "Count")
print(p)
return(p)
})
categorical_lifestyle <- lifestyle_exposome %>%
select(where(is.factor))
categorical_lifestyle_long <- pivot_longer(
categorical_lifestyle,
cols = everything(),
names_to = "variable",
values_to = "value"
)
unique_categorical_vars <- unique(categorical_lifestyle_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
data <- filter(categorical_lifestyle_long, variable == var)
p <- ggplot(data, aes(x = value, fill = value)) +
geom_bar(stat = "count") +
labs(title = paste("Distribution of", var), x = var, y = "Count")
print(p)
return(p)
})
numeric_lifestyle <- select_if(lifestyle_exposome, is.numeric)
cor_matrix <- cor(numeric_lifestyle, method = "pearson")
cor_matrix <- cor(numeric_lifestyle, method = "spearman")
corrplot(cor_matrix, method = "circle")
Chemical_Exposures <- filtered_codebook$variable_name[filtered_codebook$domain=="Chemicals"]
chemical_exposome <- exposome %>%
select(all_of(Chemical_Exposures))
summarytools::view(dfSummary(chemical_exposome, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | hs_as_c_Log2 [numeric] |
|
692 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2 | hs_cd_c_Log2 [numeric] |
|
695 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 3 | hs_co_c_Log2 [numeric] |
|
317 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 4 | hs_cs_c_Log2 [numeric] |
|
369 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 5 | hs_cu_c_Log2 [numeric] |
|
345 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 6 | hs_hg_c_Log2 [numeric] |
|
698 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 7 | hs_mn_c_Log2 [numeric] |
|
457 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 8 | hs_mo_c_Log2 [numeric] |
|
593 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 9 | hs_pb_c_Log2 [numeric] |
|
529 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 10 | hs_tl_cdich_None [factor] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 11 | hs_dde_cadj_Log2 [numeric] |
|
1050 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 12 | hs_ddt_cadj_Log2 [numeric] |
|
1039 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 13 | hs_hcb_cadj_Log2 [numeric] |
|
1036 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 14 | hs_pcb118_cadj_Log2 [numeric] |
|
1048 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 15 | hs_pcb138_cadj_Log2 [numeric] |
|
1031 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 16 | hs_pcb153_cadj_Log2 [numeric] |
|
1047 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 17 | hs_pcb170_cadj_Log2 [numeric] |
|
1039 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 18 | hs_pcb180_cadj_Log2 [numeric] |
|
1055 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 19 | hs_sumPCBs5_cadj_Log2 [numeric] |
|
1052 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 20 | hs_dep_cadj_Log2 [numeric] |
|
1045 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 21 | hs_detp_cadj_Log2 [numeric] |
|
1036 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 22 | hs_dmdtp_cdich_None [factor] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 23 | hs_dmp_cadj_Log2 [numeric] |
|
1053 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 24 | hs_dmtp_cadj_Log2 [numeric] |
|
1057 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 25 | hs_pbde153_cadj_Log2 [numeric] |
|
1036 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 26 | hs_pbde47_cadj_Log2 [numeric] |
|
1010 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 27 | hs_pfhxs_c_Log2 [numeric] |
|
1061 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 28 | hs_pfna_c_Log2 [numeric] |
|
1031 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 29 | hs_pfoa_c_Log2 [numeric] |
|
1061 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 30 | hs_pfos_c_Log2 [numeric] |
|
1050 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 31 | hs_pfunda_c_Log2 [numeric] |
|
1044 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 32 | hs_bpa_cadj_Log2 [numeric] |
|
1056 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 33 | hs_bupa_cadj_Log2 [numeric] |
|
1034 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 34 | hs_etpa_cadj_Log2 [numeric] |
|
1066 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 35 | hs_mepa_cadj_Log2 [numeric] |
|
1052 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 36 | hs_oxbe_cadj_Log2 [numeric] |
|
1069 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 37 | hs_prpa_cadj_Log2 [numeric] |
|
1031 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 38 | hs_trcs_cadj_Log2 [numeric] |
|
1053 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 39 | hs_mbzp_cadj_Log2 [numeric] |
|
1046 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 40 | hs_mecpp_cadj_Log2 [numeric] |
|
1037 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 41 | hs_mehhp_cadj_Log2 [numeric] |
|
1050 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 42 | hs_mehp_cadj_Log2 [numeric] |
|
1035 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 43 | hs_meohp_cadj_Log2 [numeric] |
|
1057 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 44 | hs_mep_cadj_Log2 [numeric] |
|
1075 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 45 | hs_mibp_cadj_Log2 [numeric] |
|
1057 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 46 | hs_mnbp_cadj_Log2 [numeric] |
|
1048 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 47 | hs_ohminp_cadj_Log2 [numeric] |
|
1085 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 48 | hs_oxominp_cadj_Log2 [numeric] |
|
1059 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 49 | hs_sumDEHP_cadj_Log2 [numeric] |
|
1028 distinct values | 0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 50 | FAS_cat_None [factor] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 51 | hs_contactfam_3cat_num_None [factor] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 52 | hs_hm_pers_None [numeric] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 53 | hs_participation_3cat_None [factor] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 54 | hs_cotinine_cdich_None [factor] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 55 | hs_globalexp2_None [factor] |
|
|
0 (0.0%) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 56 | hs_smk_parents_None [factor] |
|
|
0 (0.0%) |
Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10
#separate numeric and categorical data
numeric_chemical <- chemical_exposome %>%
select(where(is.numeric))
numeric_chemical_long <- pivot_longer(
numeric_chemical,
cols = everything(),
names_to = "variable",
values_to = "value"
)
unique_numerical_vars <- unique(numeric_chemical_long$variable)
num_plots <- lapply(unique_numerical_vars, function(var) {
data <- filter(numeric_chemical_long, variable == var)
p <- ggplot(data, aes(x = value)) +
geom_histogram(bins = 30, fill = "blue") +
labs(title = paste("Histogram of", var), x = "Value", y = "Count")
print(p)
return(p)
})
categorical_chemical <- chemical_exposome %>%
select(where(is.factor))
categorical_chemical_long <- pivot_longer(
categorical_lifestyle,
cols = everything(),
names_to = "variable",
values_to = "value"
)
unique_categorical_vars <- unique(categorical_chemical_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
data <- filter(categorical_chemical_long, variable == var)
p <- ggplot(data, aes(x = value, fill = value)) +
geom_bar(stat = "count") +
labs(title = paste("Distribution of", var), x = var, y = "Count")
print(p)
return(p)
})
numeric_chemical <- select_if(chemical_exposome, is.numeric)
cor_matrix <- cor(numeric_chemical, method = "pearson")
cor_matrix <- cor(numeric_chemical, method = "spearman")
custom_color_scale <- list(
c(0, "darkred"),
c(0.5, "white"),
c(1, "darkblue")
)
plot_ly(
z = cor_matrix,
x = colnames(cor_matrix),
y = colnames(cor_matrix),
type = "heatmap",
colorscale = custom_color_scale
) %>%
layout(
title = "Correlation Matrix",
xaxis = list(tickangle = -90),
yaxis = list(side = "left")
)
summarytools::view(dfSummary(covariates, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | |||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | ID [integer] |
|
1301 distinct values (Integer sequence) | 0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 2 | h_cohort [factor] |
|
|
0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 3 | e3_sex_None [factor] |
|
|
0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 4 | e3_yearbir_None [factor] |
|
|
0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 5 | h_mbmi_None [numeric] |
|
853 distinct values | 0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 6 | hs_wgtgain_None [numeric] |
|
49 distinct values | 0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 7 | e3_gac_None [numeric] |
|
72 distinct values | 0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 8 | h_age_None [numeric] |
|
665 distinct values | 0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 9 | h_edumc_None [factor] |
|
|
0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 10 | h_native_None [factor] |
|
|
0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 11 | h_parity_None [factor] |
|
|
0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 12 | hs_child_age_None [numeric] |
|
879 distinct values | 0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 13 | hs_c_height_None [numeric] |
|
311 distinct values | 0 (0.0%) | ||||||||||||||||||||||||||||||||||||
| 14 | hs_c_weight_None [numeric] |
|
311 distinct values | 0 (0.0%) |
Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10
#separate numeric and categorical data
numeric_covariates <- covariates %>%
select(where(is.numeric))
numeric_covariates_long <- pivot_longer(
numeric_covariates,
cols = everything(),
names_to = "variable",
values_to = "value"
)
unique_numerical_vars <- unique(numeric_covariates_long$variable)
num_plots <- lapply(unique_numerical_vars, function(var) {
data <- filter(numeric_covariates_long, variable == var)
p <- ggplot(data, aes(x = value)) +
geom_histogram(bins = 30, fill = "blue") +
labs(title = paste("Histogram of", var), x = "Value", y = "Count")
print(p)
return(p)
})
categorical_covariates <- covariates %>%
select(where(is.factor))
categorical_covariates_long <- pivot_longer(
categorical_covariates,
cols = everything(),
names_to = "variable",
values_to = "value"
)
unique_categorical_vars <- unique(categorical_covariates_long$variable)
categorical_plots <- lapply(unique_categorical_vars, function(var) {
data <- filter(categorical_covariates_long, variable == var)
p <- ggplot(data, aes(x = value, fill = value)) +
geom_bar(stat = "count") +
labs(title = paste("Distribution of", var), x = var, y = "Count")
print(p)
return(p)
})
numeric_covariate <- select_if(covariates, is.numeric)
cor_matrix <- cor(numeric_covariate, method = "pearson")
cor_matrix <- cor(numeric_covariate, method = "spearman")
corrplot(cor_matrix, method = "circle")
outcome_BMI <- phenotype %>%
select(hs_zbmi_who)
summarytools::view(dfSummary(outcome_BMI, style = 'grid', plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No | Variable | Stats / Values | Freqs (% of Valid) | Graph | Missing | ||||
|---|---|---|---|---|---|---|---|---|---|
| 1 | hs_zbmi_who [numeric] |
|
421 distinct values | 0 (0.0%) |
Generated by summarytools 1.0.1 (R version 4.4.0)
2024-06-10
First 10 rows and columns of the metabolomic serum data
load(paste0(work.dir, "/metabol_serum.RData"))
kable(metabol_serum.d[1:10,1:10], align="c", digits=2, format="pipe")
| 430 | 1187 | 940 | 936 | 788 | 698 | 380 | 196 | 114 | 885 | |
|---|---|---|---|---|---|---|---|---|---|---|
| metab_1 | -2.15 | -0.69 | -0.69 | -0.19 | -1.96 | -1.90 | -0.22 | -1.38 | -0.54 | -1.25 |
| metab_2 | -0.71 | -0.37 | -0.36 | -0.34 | -0.35 | -0.63 | -0.26 | -0.46 | -0.44 | -0.48 |
| metab_3 | 8.60 | 9.15 | 8.95 | 8.54 | 8.73 | 8.24 | 9.03 | 8.29 | 8.37 | 8.18 |
| metab_4 | 0.55 | -1.33 | -0.13 | -0.62 | -0.80 | -0.46 | 0.49 | 0.12 | -0.76 | -0.07 |
| metab_5 | 7.05 | 6.89 | 7.10 | 7.01 | 6.90 | 6.94 | 6.77 | 6.62 | 6.85 | 7.24 |
| metab_6 | 5.79 | 5.81 | 5.86 | 5.95 | 5.95 | 5.42 | 5.82 | 5.65 | 5.44 | 5.60 |
| metab_7 | 3.75 | 4.26 | 4.35 | 4.24 | 4.88 | 4.70 | 4.08 | 4.73 | 3.98 | 4.30 |
| metab_8 | 5.07 | 5.08 | 5.92 | 5.41 | 5.39 | 4.62 | 5.10 | 5.28 | 4.51 | 5.45 |
| metab_9 | -1.87 | -2.30 | -1.97 | -1.89 | -1.55 | -1.78 | -2.29 | -1.64 | -2.02 | -1.68 |
| metab_10 | -2.77 | -3.42 | -3.40 | -2.84 | -2.45 | -3.14 | -3.36 | -2.88 | -3.05 | -2.92 |